This invention relates to the field of monitoring equipment and to a system for measuring the characteristics of scatterers, that are situated in a predetermined location, by using spaced receiver remote sensors.
It is a problem in the field of monitoring equipment to accurately measure different characteristics of remotely located scatterers. The scatterers can be located in any of a number of environments, including: the atmosphere, in the ground, in a human body, or any other media, with the determined characteristics of the scatterers being used in the fields of meteorology, weather forecasting, airport environment, geology, agriculture, medicine, astronomy, and the like. Existing monitoring equipment can be divided into two classes: single receiver systems and multiple receiver systems. Each of these classes of remote sensors has advantages and disadvantages which are related to the number of receivers used and the physical limitations of the measurement environment.
A type of standard, widely used single receiver remote sensor system transmits a radio or an acoustic waves, or a light via a single transmitting antenna and a component of this signal, reflected from the scatterers, is received at the single receiving antenna to execute the measurement of characteristics of remotely located scatterers. These single receiver systems include radars, lidars, etc. and are able to measure the projection of the mean speed of scatterers on the direction of transmitted beam, which measurement is also termed the radial velocity of the scatterers. To measure the speed components of the scatterers in a direction that is normal to the direction of transmitted beam, the Velocity Azimuth Display (VAD) technique is used, with a simplified version of Velocity Azimuth Display technique, termed the Doppler Beam Swinging (DBS) technique, being broadly used in practical measurements. The drawbacks of the Velocity Azimuth Display technique and the Doppler Beam Swinging technique are well known and they include: poor spatial resolution: the swinging beam covers a significant volume of space; poor temporal resolution: measurements for at least three beam directions must be taken one after another; possible contamination of the results by ground clutter and/or hard targets when they are present; usage of strong assumptions (spatial homogeneity of the measured speed of the scatterers over the total covered volume, temporal stationarity of the speed of scatterers during all consequent measurements, etc.). These assumptions are typically invalid in the most important measurement situations for atmospheric measurements, including adverse weather conditions and/or in mountainous areas.
The drawbacks of poor spatial resolution and poor temporal resolution can be addressed by using multiple receiver systems, such as Spaced Antenna (SA) remote sensors. These multiple receiver systems have one transmitter and a plurality of antennas to receive components of the transmitted signal reflected from the scatterers (also termed xe2x80x9creturn signalsxe2x80x9d). The centers of the antennas are spatially separated, although the antennas can be located very close one to another. The second-order auto- and cross-correlation functions and/or auto- and cross-spectra for the return signals are calculated and analyzed to obtain all components of the mean speed of scatterers. There are several methods for processing the correlation functions or spectra of the return signals to obtain the mean speed of the scatterers. All correlation function or spectra based methods are strongly affected by ground clutter and/or hard targets when these signal contaminants are present, hence, the drawback of possible contamination of the results by ground clutter and/or hard targets is applicable to Spaced Antenna remote sensor systems as well, and in higher extent than to the Velocity Azimuth Display technique and the Doppler Beam Swinging technique noted above. The correlation function or spectra based methods deploy numerous assumptions about statistical characteristics of scattering field, advection speed field, spatial distribution of scatterers within the illuminated volume, etc. One can never check the validity of these assumptions. Hence, limitation of the usage of strong assumptions is also applicable to all present methods of data processing for Spaced Antenna remote sensor systems, and again in higher extent than to the Velocity Azimuth Display technique and the Doppler Beam Swinging technique. Furthermore, the correlation-function-based methods cannot be effectively applied to overlapping receivers, and this makes the signal-to-noise ratio of existing Spaced Antenna systems much lower than that for corresponding Velocity Azimuth Display technique systems and the Doppler Beam Swinging technique systems.
In addition to the limitations of the two classes of existing monitoring equipment noted above, there are other data collection difficulties encountered in measuring characteristics of scatterers. For example, the speed fluctuations with respect to the mean speed of scatterers are referred to as turbulence. Return signals from most remote sensors are spatially weighted averages over a large illuminated volume, and, consequently, over a huge number of individual scatterers. Therefore, a remote sensor system is typically unable to accurately sense fluctuations in the speed of an individual scatterer, hence, a typical remote sensor system cannot directly measure the turbulence parameters even when scatterers are xe2x80x9cidealxe2x80x9d tracers. For this reason, there is no exact relation between the parameters of turbulence and the parameters of the return signals. The first drawback of the existing methods for turbulence measurements is that all these methods are based on numerous assumptions of typically unknown validity. The second drawback is that the parameters of the return signals that are used for turbulence measurements, e.g., the spectral width, the rate of fading of correlation functions, etc. are typically estimated indirectly, and at low accuracy, especially at low signal-to-noise ratio. A further limitation of the two classes of existing monitoring equipment is that the standard approach to the identification of scatterers (their shape, orientation, material, distribution, etc.) is to use dual-polarization remote sensors. The reflectivities of differently polarized return signals are used to define the Stokes parameters (basic indicators for identification), and different combinations of the Stokes parameters are used for a specific identification. This standard approach has shown a high degree of accuracy in the identification of scatterers. The drawback of this approach is that in order to utilize it, one needs to deploy a dual-polarization remote sensor.
Therefore, existing methods for measurements of characteristics of scatterers by existing single antenna and spaced antenna remote sensors have several limitations to their abilities, and these limitations complicate the operational use of these methods.
The above described problems are solved and a technical advance achieved by the present system for measuring characteristics of scatterers using spaced receiver remote sensors which removes the limitations of poor spatial resolution and poor temporal resolution that are found in existing monitoring equipment. The key feature of the system for measuring characteristics of scatterers using spaced receiver remote sensors is the use of structure functions of the return signals instead of correlation functions or spectra of the return signals for retrieval characteristics of the scatterers from the return signals. This allows the system for measuring characteristics of scatterers using spaced receiver remote sensors to remove the above-noted limitations of: the usage of strong assumptions, the inability to use correlation-function-based methods for overlapping receivers, indirect estimation of parameters of return signals that are used for turbulence measurements, the need to deploy a dual-polarization remote sensor for identification of scatterers. The system for measuring characteristics of scatterers using spaced receiver remote sensors also significantly mitigates, if not removes, the limitation of contamination of the results by ground clutter and/or hard targets. The system for measuring characteristics of scatterers using spaced receiver remote sensors is based on a newly developed, asymptotically exact theory for the local structure of the return signals for remote sensors.
The system for measuring characteristics of scatterers using spaced receiver remote sensors addresses the drawback of deploying a dual-polarization remote sensor by providing an alternative set of indicators for the identification of scatterers, using spaced receiver remote sensors with a single polarization. These indicators can be used for identification of the characteristics of the scatterers, or can be used in combination with the Stokes parameters if the system for measuring characteristics of scatterers using spaced receiver remote sensors is able to operate in a dual-polarization mode. While the system for measuring characteristics of scatterers using spaced receiver remote sensors is unable to remove the drawback of the use of numerous assumptions of typically unknown validity, it does provide direct, unambiguous, high-accuracy measurements for the parameters of the return signals that are used to estimate turbulence and, in particular, the turbulence intensity.
The system for measuring characteristics of scatterers using spaced receiver remote sensors produces a predetermined set of scatterer characteristics, where the number and location of receivers in this system depends on the characteristics of the scatterers to be determined, although at least two receivers must be deployed, the return signals from the receivers must be collected with small enough sampling time interval and the signals from all receivers must be correlated. The system is capable of determining various characteristics of scatterers, including but not limited to: the mean speed components, turbulence intensity, size, shape, material, and the like. As shown in flow diagram form in FIG. 1, at step 101, the system is initialized by selecting the averaging time interval and update rate for results, ranges to be analyzed, and the order of structure functions to be calculated and analyzed, depending on characteristics of scatterers to be determined. The method is applied in the same way to all specified ranges over all time intervals. It is preferable to pre-process return signals from each receiver at step 102 to xe2x80x9ccleanxe2x80x9d possible contaminants from the return signals to the maximum extent without effecting the useful information contained in the return signals. Pre-processing of the return signals is accomplished similarly for signals from each receiver, and it may include (although is not limited by) any of a number of known processes: filtering of the random noise; using a clutter removal algorithm(s); removing the mean values from signals; normalizing the signals; and the like.
The auto-structure functions of specified order are then calculated at step 103 for each receiver, and coefficients in the power decomposition of these functions over time separation at the limit of time separation tends to zero are estimated. The major objective of this step is to determine the moments of noise and the effective gain factors of different order. The number of coefficients to be estimated depends on the characteristics of scatterers to be determined. However, at least the zero order coefficients must be estimated for the even order structure functions. These coefficients are necessary to estimate the moments of noise and effective gain factors. Other coefficients provide estimates of turbulence characteristics after the mean speed is estimated.
The next step is to calculate the cross-structure functions for selected pairs of receivers at step 104, and estimate coefficients in the power decomposition of these functions over time separation at the limit of time separation tends to zero. The objective of this step is to estimate all the predetermined characteristics of the scatterers. The number of coefficients to be estimated depends on the characteristics of the scatterers that are to be determined. The zero order coefficients provide indicators for identification of scatterers, the first order coefficients provide the mean speed components, the second order coefficients provide estimates for turbulence characteristics, and so on.
Post-processing is then applied at step 105 to provide the most reliable final estimates for the predetermined characteristics of the scatterers, and, if this is required, statistical errors for each characteristic as well as a measure for reliability of the estimates. This processing step can includes (although it is not limited by) statistical analysis of all obtained estimates for the predetermined characteristics of the scatterers if the remote sensor can produce several estimates for some characteristics; joint statistical analysis of the characteristics of scatterers at the analyzed time interval with those from the previous time interval(s); joint statistical analysis of the characteristics of the scatterers at the analyzed range with those from other close enough ranges; identification of scatterers in accordance with predetermined requirements by using a set of measured indicators; and so on. Post-processing is an optional operation; it may, or may not be applied.
The final and/or intermediate estimates for the predetermined characteristics of the scatterers are displayed at step 106 in any user specified format. The user-specified set of characteristics can also be archived for future use and/or reference.